Previously, we reported that the stress associated with chronic isolation was associated with increased beta-amyloid (Abeta) plaque deposition and memory deficits in the Tg2576 transgenic animal model of Alzheimer's disease (AD) [Dong H, Goico B, Martin M, Csernansky CA, Bertchume A, Csernansky JG (2004) Effects of isolation stress on hippocampal neurogenesis, memory, and amyloid plaque deposition in APP (Tg2576) mutant mice. Neuroscience 127:601-609]. In this study, we investigated the potential mechanisms of stress-accelerated Abeta plaque deposition in this Tg2576 mice by examining the relationship between plasma corticosterone levels, expression of glucocorticoid receptor (GR) and corticotropin-releasing factor receptor-1 (CRFR1) in the brain, brain tissue Abeta levels and Abeta plaque deposition during isolation or group housing from weaning (i.e. 3 weeks of age) until 27 weeks of age. We found that isolation housing significantly increased plasma corticosterone levels as compared with group-housing in both Tg+ mice (which contain and overexpress human amyloid precursor protein (hAPP) gene) and Tg- mice (which do not contain hAPP gene as control). Also, isolated, but not group-housed animals showed increases in the expression of GR in the cortex. Furthermore, the expression of CRFR1 was increased in isolated Tg+ mice, but decreased in isolated Tg- mice in both cortex and hippocampus. Changes in the components of hypothalamic-pituitary-adrenal (HPA) axis were accompanied by increases in brain tissue Abeta levels and Abeta plaque deposition in the hippocampus and overlying cortex in isolated Tg+ mice. These results suggest that isolation stress increases corticosterone levels and GR and CRFR1 expression in conjunction with increases in brain tissue Abeta levels and Abeta plaque deposition in the Tg2576 mouse model of AD.

Acupuncture therapy (AT) is a non-pharmacological method of treatment that has been applied to various neurological diseases. However, studies on its longitudinal effect on the neural mechanisms of patients with mild cognitive impairment (MCI) for treatment purposes are still lacking in the literature. In this clinical study, we assess the longitudinal effects of ATs on MCI patients using two methods: (i) Montreal Cognitive Assessment test (MoCA-K, Korean version), and (ii) the hemodynamic response (HR) analyses using functional near-infrared spectroscopy (fNIRS). fNIRS signals of a working memory (WM) task were acquired from the prefrontal cortex. Twelve elderly MCI patients and 12 healthy people were recruited as target and healthy control (HC) groups, respectively. Each group went through an fNIRS scanning procedure three times: The initial data were obtained without any ATs, and subsequently a total of 24 AT sessions were conducted for MCI patients (i.e., MCI-0: the data prior to ATs, MCI-1: after 12 sessions of ATs for 6 weeks, MCI-2: another 12 sessions of ATs for 6 weeks). The mean HR responses of all MCI-0–2 cases were lower than those of HCs. To compare the effects of AT on MCI patients, MoCA-K results, temporal HR data, and spatial activation patterns (i.e., t -maps) were examined. In addition, analyses of functional connectivity (FC) and graph theory upon WM tasks were conducted. With ATs, (i) the averaged MoCA-K test scores were improved (MCI-1, p = 0.002; MCI-2, p = 2.9 e –4 ); (ii) the mean HR response of WM tasks was increased ( p < 0.001); and (iii) the t -maps of MCI-1 and MCI-2 were enhanced. Furthermore, an increased FC in the prefrontal cortex in both MCI-1/MCI-2 cases in comparison to MCI-0 was obtained ( p < 0.01), and an increasing trend in the graph theory parameters was observed. All these findings reveal that ATs have a positive impact on improving the cognitive function of MCI patients. In conclusion, ATs can be used as a therapeutic tool for MCI patients as a non-pharmacological method (Clinical trial registration number: KCT 0002451 https://cris.nih.go.kr/cris/en/ ).

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1165This paper deals with dynamic analysis of Pipeline Inspection Gauge (PIG) flow control in natural gas pipelines. The dynamic behaviour of PIG depends on the pressure differential generated by injected gas flow behind the tail of the PIG and expelled gas flow in front of its nose. To analyze dynamic behaviour characteristics (e.g. gas flow, the PIG position and velocity) mathematical models are derived. Two types of nonlinear hyperbolic partial differential equations are developed for unsteady flow analysis of the PIG driving and expelled gas. Also, a non-homogeneous differential equation for dynamic analysis of the PIG is given. The nonlinear equations are solved by method of characteristics (MaC) with a regular rectangular grid under appropriate initial and boundary conditions. Runge-Kutta method is used for solving the steady flow equations to get the initial flow values and for solving the dynamic equation of the PIG. The upstream and downstream regions are divided into a number of elements of equal length. The sampling time and distance are chosen under Courant -Friedrich-Lewy (CFL) restriction. Simulation is performed with a pipeline segment in the Korea gas corporation (KOGAS) low pressure system, Ueijungboo-Sangye line. The simulation results show that the derived mathematical models and the proposed computational scheme are effective for estimating the position and velocity of the PIG with a given operational condition of pipeline. A: Pipe cross section area [m 2 ] c : Wave speed [m/s] C : Linear damping coefficient of PIG [Ns/m] C c : Convection heat transfer coefficient [W/m 2 K] C; : Specific heat at constant volume [J/kgK] d : Internal diameter of pipe [rn] e : Internal energy per unit mass [J/kg] • Corresponding Author, FfPsta FfPdYn r, g h f k K I L PI G m M : Darcy friction coefficient : Braking force [N] : Friction force per unit pipe length [N/m] : Friction force between PIG and pipe wall' including [N] : Static friction force : Dynamic friction force : PIG driving force [N] : Gravity acceleration [m/S2] : Pipe head loss [m] : Pipe wall roughness [m] : Wear factor per distance travel [N/m] : Length of pipeline [m] : Length of PIG [m] : Hydraulic mean radius of pipe [m] : Weight of PIG [kg] P : Flow pressure [N/m 2 ] q : Compound rate of heat inflow per unit area of pipe wall [W'/m 2 ] R : Gas constant [J/kgK] S : Perimeter of pipe [m] T : Flow temperature [K] Text : Seabed temperature [K] u : Flow velocity [m/s] x : Distance from pipe inlet [m] XPiC : Position of PIG [m] VPlC : Velocity of PIG [rn/s] Greeks r : The ratio of specific heat !.I : Kinetic viscosity of flow [m 2/s] p : Fluid density [kg/m 3 ] SubscriptsL, R, M, N, S, 0, P: The grid points, and 0, I : The points at inlet and outlet of pipeline